{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_feature_importances method used by a scikit-learn classifier\n",
    "\n",
    "In this example, we'll be plotting the feature importances in a `RandomForestClassifier` for the Iris dataset. In order for this to work, we need to first create an instance of our classifier then fit it to our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_iris as load_data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (12, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=1,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "X, y = load_data(return_X_y=True)\n",
    "\n",
    "# Create classifier instance and fit\n",
    "classifier = RandomForestClassifier(random_state=1)\n",
    "classifier.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AKmEMAACVXwkNp5fT8ddp3+50/N78qnCAexUzxgAAkDAGAIBKGAMAQCWMAQCgEsYAAFAJ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\nAABU9f8DEwy8J7uC8AcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac0f276ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.estimators.plot_feature_importances(classifier, feature_names=['petal length', 'petal width',\n",
    "                                                                     'sepal length', 'sepal width'])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
